Multimodal Event Detection in Twitter Hashtag Networks

TL;DR

This paper introduces a multimodal event detection method for Twitter that combines text and geolocation data using a generative model and EM algorithm, effectively identifying events and hashtags in large datasets.

Contribution

It proposes a novel generative latent variable model and an efficient EM-based learning algorithm for multimodal event detection in Twitter data.

Findings

Effective detection of events and hashtags in large Twitter datasets

The method is computationally efficient and scalable

Experimental results demonstrate the approach's efficacy

Abstract

Event detection in a multimodal Twitter dataset is considered. We treat the hashtags in the dataset as instances with two modes: text and geolocation features. The text feature consists of a bag-of-words representation. The geolocation feature consists of geotags (i.e., geographical coordinates) of the tweets. Fusing the multimodal data we aim to detect, in terms of topic and geolocation, the interesting events and the associated hashtags. To this end, a generative latent variable model is assumed, and a generalized expectation-maximization (EM) algorithm is derived to learn the model parameters. The proposed method is computationally efficient, and lends itself to big datasets. Experimental results on a Twitter dataset from August 2014 show the efficacy of the proposed method.